Variables:
Risk
Money
Security
Good time Help Success Proper Environment Tradition Creativity
Friends important Family important Leisure time Happiness Health (subjective) Satisfaction Freedom
Sex Age Country Wave Marital status Children Employment Education
library(data.table)
library(tidyr)
#read the data (Wave 5)
# Data of Wave 5
WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")
# Convert WV5_data-object in data.frame
WV5_data_df <- as.data.frame(WV5_data)
# show first five columns
head(WV5_data_df[, 1:5])
library(dplyr)
#rename the variables
WV5_data <- WV5_data_df %>%
rename(sex = V235, age = V237, country = V2, wave = V1, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V22, freedom = V46, marital_status = V55, children = V56, creativity = V80, money = V81, security = V82, goodtime = V83, help = V84, success = V85, risk = V86, proper = V87, environment = V88, tradition = V89, employment = V241, education = V238)
WV5_data
#select only the variables of interest
WV5_data <- WV5_data %>%
select(sex, age, country, wave, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom, marital_status, children, creativity, money, security, goodtime, help, success, risk, proper, environment, tradition, employment, education)
WV5_data
#decode the country names
countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV5_data$country_lab = countrynames$name [match(WV5_data$country, countrynames$code)]
table(WV5_data$country_lab)
Andorra Argentina Australia Brazil Bulgaria Burkina Faso
1003 1002 1421 1500 1001 1534
Canada Chile China Colombia Cyprus (G) Egypt
2164 1000 1991 3025 1050 3051
Ethiopia Finland France Georgia Germany Ghana
1500 1014 1001 1500 2064 1534
Great Britain Guatemala Hong Kong Hungary India Indonesia
1041 1000 1252 1007 2001 2015
Iran Iraq Italy Japan Jordan Malaysia
2667 2701 1012 1096 1200 1201
Mali Mexico Moldova Morocco Netherlands New Zealand
1534 1560 1046 1200 1050 954
Norway Peru Poland Romania Russia Rwanda
1025 1500 1000 1776 2033 1507
Slovenia South Africa South Korea Spain Sweden Switzerland
1037 2988 1200 1200 1003 1241
Taiwan Thailand Trinidad and Tobago Turkey Ukraine United States
1227 1534 1002 1346 1000 1249
Uruguay Viet Nam Zambia
1000 1495 1500
WV5_data
NA
NA
#Read Dataset (Wave 6)
WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata")
WV6_data <- WV6_Data_R_v20201117
print(WV6_data)
` ``{r} #rename variables
WV6_data <- WV6_data %>%
rename(wave = V1, sex = V240, age = V242,country = V2, marital_status = V57, children = V58, employment = V229, education = V248, risk = V76, money = V71, security = V72, goodtime = V73, help = V74B, success = V75, proper = V77, environment = V78, tradition = V79, creativity = V70, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V23, freedom = V55 )
#select only the variables of interest
WV6_data <- WV6_data %>%
select(wave, sex, age, country, sex, marital_status, children, employment, education, risk, money, security, goodtime, help, success, proper, environment, tradition, creativity, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom)
WV6_data
NA
#decode daraset (Wave 6)
countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country_lab = countrynames$name [match(WV6_data$country, countrynames$code)]
table(WV6_data$country_lab)
Algeria Argentina Armenia Australia Azerbaijan Belarus
1200 1030 1100 1477 1002 1535
Brazil Chile China Colombia Cyprus (G) Ecuador
1486 1000 2300 1512 1000 1202
Egypt Estonia Georgia Germany Ghana Haiti
1523 1533 1202 2046 1552 1996
Hong Kong India Iraq Japan Jordan Kazakhstan
1000 4078 1200 2443 1200 1500
Kuwait Kyrgyzstan Lebanon Libya Malaysia Mexico
1303 1500 1200 2131 1300 2000
Morocco Netherlands New Zealand Nigeria Pakistan Palestine
1200 1902 841 1759 1200 1000
Peru Philippines Poland Qatar Romania Russia
1210 1200 966 1060 1503 2500
Rwanda Singapore Slovenia South Africa South Korea Spain
1527 1972 1069 3531 1200 1189
Sweden Taiwan Thailand Trinidad and Tobago Tunisia Turkey
1206 1238 1200 999 1205 1605
Ukraine United States Uruguay Uzbekistan Yemen Zimbabwe
1500 2232 1000 1500 1000 1500
WV6_data
#combine the 2 dataset (Wave 6 + Wave 5)
WV5_data
WV6_data
data = rbind(WV5_data, WV6_data)
data
#number of countries
length(unique(data$country_lab))
[1] 80
#number of participants
nrow(data)
[1] 173540
#exclusion of participants
data = subset(data, risk > 0 & sex > 0 & age > 0 & education > 0 & employment > 0 & marital_status > 0 & children >= 0 & family_important > 0 & friends_important > 0 & leisure_time > 0 & happiness > 0 & health > 0 & satisfaction > 0 & freedom > 0 & marital_status > 0 & creativity > 0 & money > 0 & security > 0 & goodtime >0 & help > 0 & success > 0, risk > 0 & proper > 0 & environment > 0 & tradition > 0 & employment > 0 & education > 0)
data
#number of males vs females (1 = males; 2 = females)
table(data$sex)
1 2
47262 50079
#create a categorical age variable
data$agecat[data$age<20]="15-19"
data$agecat[data$age>=20 & data$age <30] = "20-29"
data$agecat[data$age>=30 & data$age <40] = "30-39"
data$agecat[data$age>=40 & data$age <50] = "40-49"
data$agecat[data$age>=50 & data$age <60] = "50-59"
data$agecat[data$age>=60 & data$age <70] = "60-69"
data$agecat[data$age>=70 & data$age <80] = "70-79"
data$agecat[data$age>=80] = "80+"
#gender variables
data$sex[data$sex == 1] <- "male"
data$sex[data$sex == 2] <- "female"
#average age of participants
mean(data$age)
[1] 40.93906
#age range
range(data$age)
[1] 15 99
#risk taking Frequency
library(ggplot2)
ggplot(data, aes(x = risk)) +
geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
labs(x = "Risk Taking", y = "Frequency", title = "Histogram of Risk Taking") +
theme_minimal()
#age frequency
ggplot(data, aes(x = age)) +
geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
labs(x = "Age", y = "Frequency", title = "Histogram of Age Distributionn") +
theme_minimal()
#age vs risk taking
ggplot(data, aes(x = agecat, y = risk)) +
geom_boxplot() +
labs(title = "Boxplot of Risk and Adventure by Age",
x = "Age",
y = "Risk and Adventure") +
theme_minimal()
NA
NA
#sex vs risk taking
ggplot(data, aes(as.factor(sex), risk))+
geom_boxplot()
#descriptive data
summary(data)
sex age country wave family_important friends_important leisure_time
Length:97341 Min. :15.00 Min. : 12.0 Min. :5.000 Min. :1.000 Min. :1.000 Min. :1.000
Class :character 1st Qu.:27.00 1st Qu.:268.0 1st Qu.:5.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Mode :character Median :39.00 Median :458.0 Median :5.000 Median :1.000 Median :2.000 Median :2.000
Mean :40.94 Mean :475.8 Mean :5.366 Mean :1.103 Mean :1.658 Mean :1.911
3rd Qu.:52.00 3rd Qu.:710.0 3rd Qu.:6.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
Max. :99.00 Max. :894.0 Max. :6.000 Max. :4.000 Max. :4.000 Max. :4.000
happiness health satisfaction freedom marital_status children creativity money
Min. :1.000 Min. :1.000 Min. : 1.000 Min. : 1.000 Min. :1.000 Min. :0.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 5.000 1st Qu.: 6.000 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:2.000 1st Qu.:3.000
Median :2.000 Median :2.000 Median : 7.000 Median : 7.000 Median :1.000 Median :2.000 Median :3.000 Median :4.000
Mean :1.905 Mean :2.093 Mean : 6.767 Mean : 7.023 Mean :2.772 Mean :1.814 Mean :2.734 Mean :3.861
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.: 8.000 3rd Qu.: 9.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:5.000
Max. :4.000 Max. :5.000 Max. :10.000 Max. :10.000 Max. :6.000 Max. :8.000 Max. :6.000 Max. :6.000
security goodtime help success risk proper environment tradition
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :-5.000 Min. :-5.000 Min. :-5.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 1.000
Median :2.000 Median :3.000 Median :2.000 Median :3.000 Median :4.000 Median : 2.000 Median : 2.000 Median : 2.000
Mean :2.378 Mean :3.247 Mean :2.298 Mean :2.957 Mean :3.816 Mean : 2.551 Mean : 2.467 Mean : 2.523
3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.: 3.000 3rd Qu.: 3.000 3rd Qu.: 3.000
Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. : 6.000 Max. : 6.000 Max. : 6.000
NA's :136 NA's :114 NA's :131
employment education country_lab agecat
Min. :1.000 Min. :1.000 Length:97341 Length:97341
1st Qu.:1.000 1st Qu.:3.000 Class :character Class :character
Median :3.000 Median :5.000 Mode :character Mode :character
Mean :3.448 Mean :5.353
3rd Qu.:5.000 3rd Qu.:7.000
Max. :8.000 Max. :9.000
#data cleaning: deletion of NAs
data = na.omit(data)
summary(data)
sex age country wave family_important friends_important leisure_time
Length:96034 Min. :15.00 Min. : 12.0 Min. :5.000 Min. :1.000 Min. :1.00 Min. :1.000
Class :character 1st Qu.:27.00 1st Qu.:268.0 1st Qu.:5.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
Mode :character Median :39.00 Median :458.0 Median :5.000 Median :1.000 Median :2.00 Median :2.000
Mean :40.92 Mean :473.7 Mean :5.367 Mean :1.103 Mean :1.66 Mean :1.912
3rd Qu.:52.00 3rd Qu.:710.0 3rd Qu.:6.000 3rd Qu.:1.000 3rd Qu.:2.00 3rd Qu.:2.000
Max. :99.00 Max. :894.0 Max. :6.000 Max. :4.000 Max. :4.00 Max. :4.000
happiness health satisfaction freedom marital_status children creativity money
Min. :1.000 Min. :1.00 Min. : 1.000 Min. : 1.000 Min. :1.000 Min. :0.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:1.00 1st Qu.: 5.000 1st Qu.: 6.000 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:2.000 1st Qu.:3.000
Median :2.000 Median :2.00 Median : 7.000 Median : 7.000 Median :1.000 Median :2.000 Median :2.000 Median :4.000
Mean :1.901 Mean :2.09 Mean : 6.775 Mean : 7.029 Mean :2.775 Mean :1.818 Mean :2.727 Mean :3.861
3rd Qu.:2.000 3rd Qu.:3.00 3rd Qu.: 8.000 3rd Qu.: 9.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:5.000
Max. :4.000 Max. :5.00 Max. :10.000 Max. :10.000 Max. :6.000 Max. :8.000 Max. :6.000 Max. :6.000
security goodtime help success risk proper environment tradition
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :-5.000 Min. :-5.000 Min. :-5.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 1.000
Median :2.000 Median :3.000 Median :2.000 Median :3.000 Median :4.000 Median : 2.000 Median : 2.000 Median : 2.000
Mean :2.372 Mean :3.242 Mean :2.291 Mean :2.952 Mean :3.814 Mean : 2.546 Mean : 2.462 Mean : 2.522
3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.: 3.000 3rd Qu.: 3.000 3rd Qu.: 3.000
Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. : 6.000 Max. : 6.000 Max. : 6.000
employment education country_lab agecat
Min. :1.000 Min. :1.000 Length:96034 Length:96034
1st Qu.:1.000 1st Qu.:3.000 Class :character Class :character
Median :3.000 Median :5.000 Mode :character Mode :character
Mean :3.451 Mean :5.351
3rd Qu.:5.000 3rd Qu.:7.000
Max. :8.000 Max. :9.000
#risk vs education
ggplot(data, aes(risk, education))+
geom_point()+
geom_smooth(method = "lm")
model = lm(risk ~ education, data = data)
summary(model)
Call:
lm(formula = risk ~ education, data = data)
Residuals:
Min 1Q Median 3Q Max
-3.0407 -1.0407 0.1681 1.2725 2.3769
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.09288 0.01225 334.1 <2e-16 ***
education -0.05219 0.00208 -25.1 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.586 on 96032 degrees of freedom
Multiple R-squared: 0.006516, Adjusted R-squared: 0.006506
F-statistic: 629.8 on 1 and 96032 DF, p-value: < 2.2e-16
ggplot(data, aes(risk, freedom))+
geom_point()+
geom_smooth(method = "lm")
model1 = lm(risk ~ freedom, data = data)
summary(model1)
Call:
lm(formula = risk ~ freedom, data = data)
Residuals:
Min 1Q Median 3Q Max
-3.107 -1.107 0.185 1.234 2.331
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.155560 0.016745 248.17 <2e-16 ***
freedom -0.048652 0.002268 -21.45 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.588 on 96032 degrees of freedom
Multiple R-squared: 0.004769, Adjusted R-squared: 0.004759
F-statistic: 460.2 on 1 and 96032 DF, p-value: < 2.2e-16
#risk distribution according to Waves 5 and 6
ggplot(data, aes(as.factor(wave), risk))+
geom_boxplot()
ggplot(data, aes(risk, age))+
geom_point()+
geom_smooth(method = "lm")
attach(data)
data$education_cat[education < 3] = "incomplete or no primary education"
data$education_cat[education > 2 & education <= 6] <- "no uni"
data$education_cat[education >= 7] <- "uni"
detach(data)
table(data$education)
1 2 3 4 5 6 7 8 9
6991 6830 13265 7495 17481 7041 15788 6957 14186
data
data$wave[data$wave == 5] <- "Wave 5"
data$wave[data$wave == 6] <- "Wave 6"
data
data1 <- subset(data, country_lab %in% c("Andorra", "Romania", "Spain"))
ggplot(data1, aes(as.factor(country_lab), risk))+
geom_boxplot()
ggplot(data1, aes(happiness, risk, color = as.factor(sex)))+
geom_point()+
geom_smooth(method = "lm", se = TRUE)
print(data$sex)
[1] "female" "female" "male" "female" "female" "male" "female" "male" "female" "female" "male" "female" "female" "male"
[15] "male" "male" "female" "female" "female" "male" "female" "female" "female" "male" "female" "male" "female" "male"
[29] "male" "male" "female" "female" "female" "female" "female" "male" "female" "female" "female" "female" "female" "female"
[43] "male" "female" "female" "male" "female" "female" "male" "female" "female" "female" "female" "male" "female" "female"
[57] "female" "male" "female" "female" "female" "male" "female" "female" "female" "male" "male" "male" "male" "male"
[71] "female" "female" "female" "male" "female" "female" "male" "male" "male" "male" "female" "female" "male" "female"
[85] "male" "male" "female" "female" "female" "female" "male" "male" "female" "male" "female" "female" "female" "female"
[99] "female" "female" "female" "male" "male" "female" "female" "female" "male" "male" "female" "female" "female" "male"
[113] "female" "male" "female" "male" "female" "female" "male" "male" "female" "female" "female" "female" "female" "male"
[127] "female" "female" "female" "male" "female" "female" "female" "female" "female" "female" "female" "female" "male" "female"
[141] "female" "male" "male" "male" "male" "female" "female" "female" "male" "male" "male" "female" "female" "male"
[155] "female" "female" "female" "female" "female" "female" "female" "female" "male" "male" "female" "male" "female" "female"
[169] "female" "female" "female" "female" "male" "female" "female" "male" "female" "female" "female" "female" "female" "male"
[183] "male" "male" "female" "female" "female" "female" "male" "female" "female" "female" "female" "female" "female" "male"
[197] "male" "male" "female" "male" "female" "male" "male" "male" "female" "female" "male" "male" "female" "male"
[211] "female" "female" "male" "female" "female" "female" "female" "female" "female" "female" "male" "male" "female" "female"
[225] "male" "female" "female" "female" "male" "male" "female" "male" "female" "male" "female" "male" "male" "female"
[239] "female" "male" "female" "female" "female" "male" "female" "female" "female" "male" "female" "female" "female" "female"
[253] "male" "female" "female" "male" "male" "female" "female" "male" "female" "male" "female" "female" "female" "female"
[267] "female" "male" "male" "male" "male" "female" "female" "male" "male" "female" "male" "male" "female" "male"
[281] "female" "male" "male" "female" "female" "female" "female" "female" "male" "female" "female" "female" "female" "female"
[295] "female" "female" "female" "male" "male" "female" "male" "male" "male" "female" "female" "male" "male" "male"
[309] "female" "female" "female" "male" "female" "male" "male" "male" "female" "female" "female" "male" "female" "female"
[323] "female" "male" "male" "male" "male" "male" "male" "male" "female" "male" "male" "male" "male" "female"
[337] "male" "female" "female" "male" "female" "male" "male" "female" "female" "female" "female" "male" "female" "male"
[351] "male" "female" "female" "female" "male" "male" "male" "male" "female" "female" "female" "female" "male" "male"
[365] "male" "male" "female" "female" "male" "female" "female" "male" "female" "female" "female" "female" "female" "male"
[379] "male" "female" "male" "female" "male" "female" "female" "male" "male" "male" "male" "male" "male" "female"
[393] "male" "female" "male" "male" "female" "male" "male" "male" "female" "male" "male" "male" "female" "female"
[407] "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "female"
[421] "male" "male" "male" "female" "male" "male" "female" "male" "male" "male" "male" "male" "male" "male"
[435] "female" "male" "male" "male" "male" "female" "male" "female" "male" "female" "male" "female" "female" "female"
[449] "male" "female" "female" "male" "female" "female" "female" "female" "female" "female" "male" "female" "female" "female"
[463] "male" "male" "female" "female" "male" "male" "female" "female" "male" "male" "female" "female" "male" "male"
[477] "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "female" "male" "male"
[491] "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male"
[505] "male" "male" "male" "male" "male" "male" "male" "male" "female" "female" "male" "female" "male" "male"
[519] "male" "male" "male" "male" "male" "male" "female" "female" "male" "female" "female" "female" "male" "male"
[533] "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male"
[547] "female" "female" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "male" "female"
[561] "male" "female" "male" "female" "female" "female" "female" "female" "male" "male" "female" "male" "male" "female"
[575] "female" "female" "female" "male" "female" "male" "female" "female" "female" "male" "male" "female" "female" "female"
[589] "female" "female" "female" "male" "male" "female" "male" "male" "male" "female" "female" "female" "female" "female"
[603] "male" "male" "female" "female" "female" "male" "male" "female" "female" "female" "female" "female" "female" "female"
[617] "female" "male" "female" "female" "male" "male" "female" "female" "female" "male" "female" "female" "female" "female"
[631] "male" "female" "female" "female" "female" "female" "female" "female" "female" "female" "male" "male" "female" "male"
[645] "female" "male" "male" "female" "male" "male" "male" "female" "female" "female" "female" "female" "female" "female"
[659] "female" "male" "female" "male" "male" "female" "female" "female" "male" "female" "female" "male" "female" "female"
[673] "female" "female" "female" "female" "female" "female" "female" "female" "female" "female" "female" "female" "female" "female"
[687] "female" "male" "female" "female" "male" "female" "male" "female" "male" "female" "female" "female" "female" "male"
[701] "female" "female" "female" "female" "female" "female" "female" "female" "female" "male" "female" "female" "female" "female"
[715] "female" "female" "female" "female" "female" "female" "female" "male" "female" "female" "female" "female" "female" "male"
[729] "female" "female" "male" "female" "female" "female" "male" "female" "female" "female" "female" "female" "male" "male"
[743] "male" "male" "female" "female" "male" "female" "male" "female" "female" "female" "male" "female" "male" "female"
[757] "female" "female" "female" "male" "male" "female" "female" "female" "male" "female" "male" "female" "male" "female"
[771] "male" "male" "male" "male" "female" "female" "female" "female" "male" "female" "male" "female" "male" "male"
[785] "female" "female" "male" "female" "female" "female" "female" "male" "female" "male" "female" "female" "male" "male"
[799] "female" "female" "male" "female" "male" "female" "male" "male" "male" "male" "male" "female" "female" "male"
[813] "female" "female" "male" "female" "male" "female" "female" "female" "male" "male" "male" "female" "male" "female"
[827] "male" "male" "male" "male" "female" "male" "male" "female" "female" "female" "male" "male" "male" "male"
[841] "male" "male" "female" "female" "male" "female" "male" "male" "female" "male" "female" "female" "male" "male"
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```